feat: Warn on incompatible TimeUnit in pandas<2

narwhals/_pandas_like/utils.py

@@ -16,12 +16,14 @@
     SECONDS_PER_DAY,
     US_PER_SECOND,
 )
+from narwhals._exceptions import issue_warning
 from narwhals._utils import (
     Implementation,
     Version,
     _DeferredIterable,
     check_columns_exist,
     isinstance_or_issubclass,
+    requires,
 )
 from narwhals.exceptions import ShapeError
 
@@ -493,10 +495,21 @@ def narwhals_to_native_dtype(  # noqa: C901, PLR0912
     if into_pd_type := NW_TO_PD_DTYPES_BACKEND.get(base_type):
         return into_pd_type[dtype_backend]
     if isinstance_or_issubclass(dtype, dtypes.Datetime):
-        # Pandas does not support "ms" or "us" time units before version 2.0
         if is_pandas_or_modin(implementation) and PANDAS_VERSION < (
             2,
         ):  # pragma: no cover
+            if isinstance(dtype, dtypes.Datetime) and dtype.time_unit != "ns":
+                found = requires._unparse_version(PANDAS_VERSION)
+                available = f"available in 'pandas>=2.0', found version {found!r}."
+                changelog_url = "https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html#construction-with-datetime64-or-timedelta64-dtype-with-unsupported-resolution"
+                msg = (
+                    f"`nw.Datetime(time_unit={dtype.time_unit!r})` is only {available}\n"
+                    "Narwhals has fallen back to using `time_unit='ns'` to avoid an error.\n\n"
+                    "Hint: to avoid this warning, consider either:\n"
+                    f"- Upgrading pandas: {changelog_url}\n"
+                    f"- Using a bare `nw.Datetime`, if this precision is not important"
+                )
+                issue_warning(msg, UserWarning)
             dt_time_unit = "ns"
         else:
             dt_time_unit = dtype.time_unit

tests/dtypes_test.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import enum
+from contextlib import AbstractContextManager, nullcontext as does_not_warn
 from datetime import datetime, timedelta, timezone
 from typing import TYPE_CHECKING, Any, Literal
 
@@ -17,7 +18,7 @@
     from collections.abc import Iterable
 
     from narwhals.typing import IntoSeries, NonNestedDType
-    from tests.utils import Constructor
+    from tests.utils import Constructor, ConstructorPandasLike
 
 
 @pytest.mark.parametrize("time_unit", ["us", "ns", "ms"])
@@ -558,3 +559,26 @@ def test_dtype_base_type_nested() -> None:
     assert nw.Array.base_type() is nw.Array(nw.String, 2).base_type()
     assert nw.Struct.base_type() is nw.Struct({"a": nw.Boolean}).base_type()
     assert nw.Enum.base_type() is nw.Enum(["beluga", "narwhal"]).base_type()
+
+
+@pytest.mark.parametrize(
+    ("dtype", "context"),
+    [
+        (nw.Datetime("ns"), does_not_warn()),
+        (nw.Datetime, does_not_warn()),
+        (nw.Datetime(), pytest.warns(UserWarning, match="time_unit")),
+        (nw.Datetime("us"), pytest.warns(UserWarning, match="time_unit='us'")),
+        (nw.Datetime("s"), pytest.warns(UserWarning, match="time_unit='s'")),
+    ],
+)
+def test_pandas_datetime_ignored_time_unit_warns(
+    constructor_pandas_like: ConstructorPandasLike,
+    dtype: nw.Datetime | type[nw.Datetime],
+    context: AbstractContextManager[Any],
+) -> None:
+    data = {"a": [datetime(2001, 1, 1), None, datetime(2001, 1, 3)]}
+    expr = nw.col("a").cast(dtype)
+    df = nw.from_native(constructor_pandas_like(data))
+    ctx = does_not_warn() if PANDAS_VERSION >= (2,) else context
+    with ctx:
+        df.select(expr)